New Variant Of Particle Swarm Optimization Research Articles

Random reselection particle swarm optimization for optimal design of solar photovoltaic modules

Renewable energy is becoming more popular due to environmental concerns about the previous energy source. Accurate solar photovoltaic system model parameters substantially impact the efficiency of solar energy conversion to electricity. In this matter, swarm and evolutionary optimization algorithms have been widely utilized in dealing with practical problems due to their more straightforward concepts, efficacy, flexibility, and easy-to-implement procedural frameworks. However, the nonlinearity and complexity of the photovoltaic parameter identification caused swarm and evolutionary optimizers to exhibit Immaturity in the obtained solutions. To deal with such concerns on immature convergence and imbalanced searching trends, in this paper, we proposed the PSOCS algorithm based on the core components of particle swarm optimization (PSO) and the strategy of random reselection of parasitic nests that appeared in the cuckoo search. The parameters of the single-diode model and the double-diode model are identified based on several experiments. Based on the comprehensive comparisons, results indicate that the developed PSOCS algorithm has higher convergence accuracy and better stability than the original PSO, the original cuckoo search, and other studied algorithms. The findings indicate that we suggest the PSOCS algorithm as an enhanced and efficient approach for dealing with parameter extraction of solar photovoltaic modules. We think this new variant of PSO can be employed as a tool for the optimal designing of photovoltaic systems.

A Multi-Swarm Structure for Particle Swarm Optimization: Solving the Welded Beam Design Problem

Several studies exist in the literature that utilized metaheuristics and nature-inspired algorithms to solve engineering problems. Particle Swarm Optimization (PSO) is a well-known nature-inspired algorithm that has been used for different optimization problems due to its simplicity and ability to find near-optimal solutions. However, PSO suffers from a problem of balancing between the global search and local search abilities when applied to engineering problems. Recently, a new variant of PSO based on a novel multi-swarm architecture called Multi-swarm Particle Swarm Optimization (MPSO) was proposed. The proposed MPSO was evaluated on solving normal and large-scale problems. This study evaluated the possibility of using MPSO to simulate Welded Beam Design (WDB) problem which is a mechanical engineering problem. Several simulations were performed using the proposed approach and from the results, MPSO model was observed to simulate WBD problem with better optimal solution compared to the standalone PSO. The outcome of this study further confirmed the robustness of the MPSO over the other known metaheuristics. Generally, MPSO achieved an excellent optimization performance with a fast convergence learning process.

Particle swarm optimization with an enhanced learning strategy and crossover operator

Particle Swarm Optimization (PSO) is a well-known swarm intelligence (SI) algorithm employed for solving various optimization problems. This algorithm suffers from premature convergence to local optima. Accordingly, a number of PSO variants have been proposed in the literature. These algorithms exploited different schemes to improve performance. In this paper, we propose a new variant of PSO with an enhanced Learning strategy and Crossover operator (PSOLC). This algorithm applies three strategies, comprising altering the exemplar particles, updating the PSO parameters, and integrating PSO with Genetic Algorithm (GA). In the proposed learning strategy, each particle is guided by the best positions (pbests) of all particles, which improves its search capability. Furthermore, the proposed parameter updating scheme computes the self-cognition coefficient for each particle based on the quality of the pbests. Finally, the proposed crossover operator injects randomness to particles to improve the global search ability. The proposed improvements in PSOLC increase its exploration capability at the early stages of the search process and its exploitation ability at the end. The derived outcome from applying PSOLC and other variants of PSO to the benchmark functions verify the superiority of the proposed algorithm in terms of accuracy and convergence speed.

Particle swarm optimization with neighborhood-based budget allocation

The standard particle swarm optimization (PSO) algorithm allocates the total available budget of function evaluations equally and concurrently among the particles of the swarm. In the present work, we propose a new variant of PSO where each particle is dynamically assigned different computational budget based on the quality of its neighborhood. The main goal is to favor particles with high-quality neighborhoods by asynchronously providing them with more function evaluations than the rest. For this purpose, we define quality criteria to assess a neighborhood with respect to the information it possesses in terms of solutions’ quality and diversity. Established stochastic techniques are employed for the final selection among the particles. Different variants are proposed by combining various quality criteria in a single- or multi-objective manner. The proposed approach is assessed on widely used test suites as well as on a set of real-world problems. Experimental evidence reveals the efficiency of the proposed approach and its competitiveness against other PSO-based variants as well as different established algorithms.

Multiple Sequence Alignment with Hidden Markov Models Learned by Random Drift Particle Swarm Optimization.

Hidden Markov Models (HMMs) are powerful tools for multiple sequence alignment (MSA), which is known to be an NP-complete and important problem in bioinformatics. Learning HMMs is a difficult task, and many meta-heuristic methods, including particle swarm optimization (PSO), have been used for that. In this paper, a new variant of PSO, called the random drift particle swarm optimization (RDPSO) algorithm, is proposed to be used for HMM learning tasks in MSA problems. The proposed RDPSO algorithm, inspired by the free electron model in metal conductors in an external electric field, employs a novel set of evolution equations that can enhance the global search ability of the algorithm. Moreover, in order to further enhance the algorithmic performance of the RDPSO, we incorporate a diversity control method into the algorithm and, thus, propose an RDPSO with diversity-guided search (RDPSO-DGS). The performances of the RDPSO, RDPSO-DGS and other algorithms are tested and compared by learning HMMs for MSA on two well-known benchmark data sets. The experimental results show that the HMMs learned by the RDPSO and RDPSO-DGS are able to generate better alignments for the benchmark data sets than other most commonly used HMM learning methods, such as the Baum-Welch and other PSO algorithms. The performance comparison with well-known MSA programs, such as ClustalW and MAFFT, also shows that the proposed methods have advantages in multiple sequence alignment.

Quadratic interpolation based orthogonal learning particle swarm optimization algorithm

Particle swarm optimization (PSO) is a population based algorithm for solving global optimization problems. Owing to its efficiency and simplicity, PSO has attracted many researchers' attention and developed many variants. Orthogonal learning particle swarm optimization (OLPSO) is proposed as a new variant of PSO that relies on a new learning strategy called orthogonal learning strategy. The OLPSO differs in the utilization of the information of experience from the standard PSO, in which each particle utilizes its historical best experience and globally best experience through linear summation. In OLPSO, particles can fly in better directions by constructing an efficient exemplar through orthogonal experimental design. However, the global version based orthogonal learning PSO (OLPSO-G) still have some drawbacks in solving some complex multimodal function optimization. In this paper, we proposed a quadratic interpolation based OLPSO-G (QIOLPSO-G), in which, a quadratic interpolation based construction strategy for the personal historical best experience is applied. Meanwhile, opposition-based learning, and Gaussian mutation are also introduced into this paper to increase the diversity of the population and discourage the premature convergence. Experiments are conducted on 16 benchmark problems to validate the effectiveness of the QIOLPSO-G, and comparisons are made with four typical PSO algorithms. The results show that the introduction of the three strategies does enhance the effectiveness of the algorithm.

A variant of the particle swarm optimization for the improvement of fault diagnosis in industrial systems via faults estimation

This paper proposes an approach for Fault Diagnosis and Isolation (FDI) on industrial systems via faults estimation. FDI is presented as an optimization problem and it is solved with Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms. Also, is presented a study of the influence of some parameters from PSO and ACO in the desirable characteristics of FDI, i.e. robustness and sensitivity. As a consequence, the Particle Swarm Optimization with Memory (PSO-M) algorithm, a new variant of PSO was developed. PSO-M has the objective of reducing the number of iterations/generations that PSO needs to execute in order to provide a reasonable quality diagnosis. The proposed approach is tested using simulated data from a DC Motor benchmark. The results and analysis indicate the suitability of the approach as well as the PSO-M algorithm.

Boid particle swarm optimisation

Particle swarm optimisation (PSO) is a novel population-based stochastic optimisation algorithm inspired by the Reynolds' boid model. The original biological background of boid obeys three basic simple steering rules: separation, alignment and cohesion. However, to promote a simple update equation, none of these rules of boid model is employed by PSO methodology. Due to the weakness of biological background of PSO, in this paper, a new variant of PSO, boid particle swarm optimisation (BPSO), is designed in which cohesion rule and alignment rule are both employed to improve the performance. In BPSO, each particle has two motions: divergent motion and convergent motion. For divergent motion, each particle adjusts its moving direction according to the alignment direction and the cohesion direction, as well as in convergent motion, the original update equation of the standard version of PSO is used. To make a motion transition, a threshold is introduced to make the divergent motion is employed in the first period, whereas the convergent motion is used in the final stage. To testify the efficiency, several unconstrained benchmarks are used to compare. Simulation results show the proposed variant is more effective and efficient than other two variants of PSO when solving multi-modal high-dimensional numerical problems.